4.6 Article

A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Journal

SENSORS
Volume 21, Issue 24, Pages -

Publisher

MDPI
DOI: 10.3390/s21248204

Keywords

neural networks; Apparent Beacon Position Estimation; positioning systems; calibration; ultra wide band; non line of sight

Funding

  1. Polish National Agency for Academic Exchange [PPN/ULM/2019/1/00354/U/00001]
  2. Ministry of Education, Science and Technological Development of the Serbian Government [451-03-9/2021-14/200105]
  3. Science Fund of the Republic of Serbia [6523109]
  4. Polish Ministry of Science and Higher Education [WZ/WE-IA/4/2020]

Ask authors/readers for more resources

A novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) is proposed to improve the accuracy of lateration positioning systems. The method employs a two-step position correction pipeline and shows potential for achieving high accuracy in different indoor scenarios.
Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available